Data visualization is a key component to any scientific journal or popular science article. Being able to tell a compelling story using just the data at hand should be the goal of any figure.

In this primer, we are going to be using the package “ggplot2” in R for graphing purposes. This will get use through the basics with a few various types of figures and how to cleanly modify them for to look a bit better. Other primers will be focusing more on the components of good figure making practices.

For this primer, you will need the city_df.csv file

ggplot2 - Grammar of graphics

ggplot2 wiki

ggplot2 is one of the most widely used R packages and graphical programs. It breaks everything into scales and layers, allowing quick manipulaion of complex figure types. While it is very easy to get simple figures made, customization requires a more in-depth understanding of what is happening under the hood. Once you understand how to format your data in a manner ggplot works well with, it becomes largely intuitive.

Resources:

Below is a all open-source book and course all on data visualization in R
- https://clauswilke.com/dataviz/
- https://wilkelab.org/SDS375/

R basics for those needing a refresher:
- https://rpubs.com/chalsch/intro_to_R

Install R and RStudio Desktop

Both these programs are free and open-source. R is the actual program while RStudio is a user-friendly GUI (graphical user interface) to run R.

It is possible to set up an R Jupyter Notebooks but it the interface of RStudio is much more user friendly

Install R packages

Within R, you will need a few packages:

Wide vs Long data formats

Formating your data for figures or analysis is critical. The two formats are known as long and wide formats. Most statistical packages and ggplot require long data formats. Wide data is commonly seen in species composition data with a row per each observation at a site/whatever and a column for each species. The data estimate would be the count of that species at a site. The long verision of this would be a single count column and species column. This way we can just put in one variable to get summaries for all the species.

Example of wide and long data:
notice the difference in the diminsions

WIDE

## [1] "Wide dimensions: rows = 480 ,cols = 149"
##    site year precip block nitrogen plot Agropyron.smithii Alliium.textile
## 1 mesic 2013  729.1     A      0.0    6                 0               0
## 2 mesic 2013  729.1     A      2.5    8                 0               0
## 3 mesic 2013  729.1     A      5.0    5                 0               0
## 4 mesic 2013  729.1     A      7.5    7                 0               0
## 5 mesic 2013  729.1     A     10.0    3                 0               0
##   Ambrosia.artemisiifolia Ambrosia.psilostachya
## 1                       0                     8
## 2                       0                     7
## 3                       0                     4
## 4                       0                     0
## 5                       0                     1

WIDE

## [1] "Long dimensions: rows = 5007 ,cols = 8"
##     site year precip block nitrogen plot                    species cover
## 1  mesic 2013  729.1     A       30    1      Ambrosia psilostachya    12
## 2  mesic 2013  729.1     A       30    1              Dalea candida     4
## 3  mesic 2013  729.1     A       30    1           Panicum virgatum    25
## 4  mesic 2013  729.1     A       30    1 Dichanthelium oligosanthes     7
## 5  mesic 2013  729.1     A       30    1        Andropogon gerardii    55
## 6  mesic 2013  729.1     A       30    1       Kuhnia eupatorioides     2
## 7  mesic 2013  729.1     A       30    1            Physalis pumila     2
## 8  mesic 2013  729.1     A       30    1           Sporobolus asper     4
## 9  mesic 2013  729.1     A       30    1        Solidago canadensis     6
## 10 mesic 2013  729.1     A       30    1     Asclepias verticillata     3

Understanding your data

For this and the rest of the primer, you will need the city_df.csv

city_df.csv can be read in as is and contains various information on the 20 largest cities in WA, OR, CA, NV, and AZ.

Variable descriptions:
- State: two-letter abbreviations for the states
- City: name of city
- Lat: latitude of city
- Long: longitude of city
- Pop: city population
- Time: arbitary time series column 1-20 (simulated for graphical purposes)
- Growth: arbitary growth column (simulated for graphical purposes)
- Ppt: Annual preciptication using PRISM 30-year normals, 1981-2010
- Temp: Annual mean temperature using PRISM 30-year normals, 1981-2010
- Size: categorical city size variable based on population (small < 1,000,000 & large > 1,000,000)

Import libraries and set working directory

Working directory should be the same location as this Markdown and all other files.

library(tidyverse)
library(ggforce)
library(ggsci)
library(patchwork)
library(Hmisc)

setwd("~/g/projects/DataVis/JulieClass2022/")

Read in the city_df.csv data frame and look at the structure

city_df <- read.csv("city_df.csv")
str(city_df)
## 'data.frame':    100 obs. of  10 variables:
##  $ State : Factor w/ 5 levels "AZ","CA","NV",..: 4 4 4 4 4 4 4 4 4 4 ...
##  $ City  : Factor w/ 100 levels "Albany","Aloha",..: 66 22 73 50 10 33 35 7 19 84 ...
##  $ Lat   : num  45.5 44.1 44.9 42.3 44.1 ...
##  $ Long  : num  -123 -123 -123 -123 -121 ...
##  $ Pop   : int  2074775 273439 266804 170876 109802 109381 109128 99037 67467 63230 ...
##  $ Time  : int  20 19 18 17 16 15 14 13 12 11 ...
##  $ Growth: num  11820 10865 9689 8430 7556 ...
##  $ Ppt   : num  1104 1137 1009 509 293 ...
##  $ Temp  : num  11.92 11.35 11.67 12.12 8.12 ...
##  $ Size  : Factor w/ 2 levels "large","small": 1 2 2 2 2 2 2 2 2 2 ...

Factors are a special case variable in R that can be very helpful and also, create a huge headache. If you are getting an error in R, factor is probably the first thing you should be checking.

Let’s look under the hood of the State factor…

From the above structure view, you can see that State contains 5 total levels. If we look state as.character() variable, you will see just the state id.

as.character(city_df$State)[1:5]
## [1] "OR" "OR" "OR" "OR" "OR"

But if we look more closely as the factors and treat them as.numeric(), will see the what the factor designation is doing

as.numeric(city_df$State)[1:5]
## [1] 4 4 4 4 4

factors code discrete variables with numbers ordered alphabetically

This is very important and will come in use later with more complex figure making

Basics of ggplot layout

aesthetics or aes()
ggplot maps variable to what is known as aesthetics or aes(). Within the aesthetics you will be assigning elements of your plot such as position (x,y), color, fill, shape, and size. These will be automatically adjusted based on your data.

For example, within city_df we want to plot Time vs. Growth:

ggplot(data=city_df,aes(x=Time,y=Growth))

This will set up the plot with the basic form but we need to tell ggplot how we want to plot it.

layers
ggplot also work with layers, meaning it will build on itself in a layered/sequential manner. Let’s make some basic scatterplots with city_df to see.

Scatter / Line plot - continuous vs continuous

Functions
- geom_point(): adds points
- geom_line(): adds lines
- stat_smooth(): adds various trendlines

basic scatter plot

ggplot(data = city_df, aes(x = Time, y = Growth)) + geom_point()

scatter plot but lets add colors for each state

ggplot is really smart, if you tell it the right things. It will automatically group things based off the structure or factor in your dataset and pick colors already for you.

We will do this in the aes() in the top layer so it stays throughout the rest of the figure. Notice how it sorts alphabetically because of the factor

ggplot(data = city_df, aes(x = Time, y = Growth, color = State)) +
    geom_point()

add line for each state

ggplot already knows we want to group based on state because we said so in the top layer, so we just add a line

ggplot(data = city_df, aes(x = Time, y = Growth, color = State)) +
    geom_point() + geom_line()

add a trend line instead of a line between each point

stat_smooth common options:
- method = "": chooses the type of model to run to get trend line (loess,lm,etc.)
- se = TRUE: standard error background based off 95% bootstrapped confidence inteval

Google for the rest of the options

ggplot(data = city_df, aes(x = Time, y = Growth, colour = State)) +
    geom_point() + stat_smooth()

Let’s get a trend line for the entire model, instead of each state BUT keep state colours.

hint: remember the layering

We can do this by confining the colour aesthetic within geom_point()

ggplot(data = city_df, aes(x = Time, y = Growth)) + geom_point(aes(colour = State)) +
    stat_smooth()

Change the size and colour and linetype of things independently to make things look a little nicer

Remember, ordering matters. Sometimes it might be nice for lines to be behind points, that is up to you.

ggplot(data = city_df, aes(x = Time, y = Growth)) + geom_point(aes(colour = State),
    size = 4) + stat_smooth(linetype = "dashed", colour = "black",
    size = 2)

Add a shape for population Size

It automatically picks shapes for you based off factors and puts it in the legend.

ggplot(data = city_df, aes(x = Time, y = Growth)) + geom_point(aes(colour = State,
    shape = Size), size = 4) + stat_smooth(linetype = "dashed",
    colour = "black", size = 2)

repurposing code for similiar figures

If you find a format you like, just use it as a template and change variables around.

Lets make figures for:
- Lat vs. Ppt
- Lat vs. Temp
- Temp vs. Ppt

Let’s also pick a linear trend line over a curved loess

# lat vs Ppt
ggplot(data = city_df, aes(x = Lat, y = Ppt)) + geom_point(aes(colour = State,
    shape = Size), size = 4) + stat_smooth(method = "lm", linetype = "dashed",
    colour = "black", size = 2)

# lat vs Temp
ggplot(data = city_df, aes(x = Lat, y = Temp)) + geom_point(aes(colour = State,
    shape = Size), size = 4) + stat_smooth(method = "lm", linetype = "dashed",
    colour = "black", size = 2)

# Temp vs Ppt
ggplot(data = city_df, aes(x = Temp, y = Ppt)) + geom_point(aes(colour = State,
    shape = Size), size = 4) + stat_smooth(method = "lm", linetype = "dashed",
    colour = "black", size = 2)

Categorical Variables in figures

Let’s try some various plots using categorical predictors and a continuous response.

Basic functions
- geom_bar(): adds bars
- geom_point(): adds points
- geom_error(): adds errorbars
- geom_boxblot(): adds boxplot
- geom_violin(): adds violin plot
- geom_density(): adds density plot

Fancy functions
- stat_summary(): summarizes and plots data
- geom_sina(): in ggforce libray, plots raw in violin/density shape

more aesthestic functions
- facet_wrap(): creates panels for different variables

If we are going to be using the base functions, we must first summarize the data in a meaningful way (mean, sd, N, 95% CI). Let’s plot Ppt and Temp by state

city_sum_df <- city_df %>%
  group_by(State) %>%
  summarise(Temp_mean = mean(Temp, na.rm = TRUE), #temp mean
            Temp_sd = sd(Temp, na.rm = TRUE), #temp standard deviation
            Temp_n = n(), #temp count
            Ppt_mean = mean(Ppt, na.rm = TRUE), #Ppt mean
            Ppt_sd = sd(Ppt, na.rm = TRUE),  #Ppt standard deviation
            Ppt_n = n()) %>% #Ppt count 
  mutate(Temp_se = Temp_sd / sqrt(Temp_n), #Temp standard error
         Temp_lower.ci = Temp_mean - qt(1 - (0.05 / 2), Temp_n - 1) * Temp_se, #Temp lower 95% confidence interval
         Temp_upper.ci = Temp_mean + qt(1 - (0.05 / 2), Temp_n - 1) * Temp_se, #Temp upper 95% confidence interval
         Ppt_se = Ppt_sd / sqrt(Ppt_n), #Ppt standard error
         Ppt_lower.ci = Ppt_mean - qt(1 - (0.05 / 2), Ppt_n - 1) * Ppt_se, #Ppt lower 95% confidence interval
         Ppt_upper.ci = Ppt_mean + qt(1 - (0.05 / 2), Ppt_n - 1) * Ppt_se) #Ppt upper 95% confidence interval

city_sum_df
## # A tibble: 5 × 13
##   State Temp_mean Temp_sd Temp_n Ppt_mean Ppt_sd Ppt_n Temp_se Temp_lower.ci
##   <fct>     <dbl>   <dbl>  <int>    <dbl>  <dbl> <int>   <dbl>         <dbl>
## 1 AZ         20.9   3.56      20     249.   88.0    20   0.797          19.2
## 2 CA         17.2   1.93      20     355.  147.     20   0.431          16.3
## 3 NV         16.4   4.53      20     156.   54.2    20   1.01           14.3
## 4 OR         11.4   1.20      20     945.  269.     20   0.269          10.9
## 5 WA         10.9   0.776     20     812.  378.     20   0.174          10.5
## # … with 4 more variables: Temp_upper.ci <dbl>, Ppt_se <dbl>,
## #   Ppt_lower.ci <dbl>, Ppt_upper.ci <dbl>

Uh oh, what if we want to put this all on the same figure… We have a wide vs long error, let’s fix it

#### TEMP ####
Temp_sum_df <- city_df %>%
  group_by(State) %>%
  summarise(mean = mean(Temp, na.rm = TRUE), #temp mean
            sd = sd(Temp, na.rm = TRUE), #temp standard deviation
            n = n()) %>% #temp count
  mutate(se = sd / sqrt(n), #Temp standard error
         ci = 1.96 * se) #Temp 95% confidence interval

Temp_sum_df$Clim <- 'Temp'

#### PPT ####
Ppt_sum_df <- city_df %>%
  group_by(State) %>%
  summarise(mean = mean(Ppt, na.rm = TRUE), #Ppt mean
            sd = sd(Ppt, na.rm = TRUE), #Ppt standard deviation
            n = n()) %>% #Ppt count
  mutate(se = sd / sqrt(n), #Ppt standard error
         ci = 1.96*se) #Ppt 95% confidence interval

Ppt_sum_df$Clim <- 'Ppt'

#### combine data.frames ####
city_sum_df <- rbind(Temp_sum_df,Ppt_sum_df)
city_sum_df
## # A tibble: 10 × 7
##    State  mean      sd     n     se      ci Clim 
##    <fct> <dbl>   <dbl> <int>  <dbl>   <dbl> <chr>
##  1 AZ     20.9   3.56     20  0.797   1.56  Temp 
##  2 CA     17.2   1.93     20  0.431   0.845 Temp 
##  3 NV     16.4   4.53     20  1.01    1.99  Temp 
##  4 OR     11.4   1.20     20  0.269   0.527 Temp 
##  5 WA     10.9   0.776    20  0.174   0.340 Temp 
##  6 AZ    249.   88.0      20 19.7    38.6   Ppt  
##  7 CA    355.  147.       20 32.8    64.4   Ppt  
##  8 NV    156.   54.2      20 12.1    23.8   Ppt  
##  9 OR    945.  269.       20 60.1   118.    Ppt  
## 10 WA    812.  378.       20 84.5   166.    Ppt

Much better, lets make a bar chart now

We much identify the stat with in geom_bar to be ‘identity’ as the default is to be counts (like a histogram)

ggplot(data = city_sum_df, aes(x = State, y = mean)) + geom_bar(stat = "identity")

but wait, we have both Temp and Ppt in there. Lets colour them differently with ‘fill’

ggplot(data = city_sum_df, aes(x = State, y = mean, fill = Clim)) +
    geom_bar(stat = "identity")

That also didnt work. We need to tell ggplot exactly where we want them to be

We will do this with position()

ggplot(data = city_sum_df, aes(x = State, y = mean, fill = Clim)) +
    geom_bar(stat = "identity", position = "dodge")

#### Let’s use facet_wrap() instead to get a better look at the comparisons among states

options:
- nrow: number of rows
- ncol: number of columns
- scales=‘free’: different y scales for each panel (be careful… can be misleading)

ggplot(data = city_sum_df, aes(x = State, y = mean, fill = Clim)) +
    facet_wrap(~Clim, nrow = 1, scales = "free") + geom_bar(stat = "identity")

Add errorbars manually

What would we want errorbars to be to effectively show differences? (sd, se, or 95% CI)

ggplot(data = city_sum_df, aes(x = State, y = mean, fill = Clim)) +
    facet_wrap(~Clim, nrow = 1, scales = "free") + geom_bar(stat = "identity") +
    geom_errorbar(aes(ymin = mean - ci, ymax = mean + ci))

Use points instead of bars

ggplot(data = city_sum_df, aes(x = State, y = mean, colour = Clim)) +
    facet_wrap(~Clim, nrow = 1, scales = "free") + geom_point(size = 5) +
    geom_errorbar(aes(ymin = mean - ci, ymax = mean + ci), colour = "black")

That is a henious way to summarize the data

Let’s use some different built in functions within ggplot to summarize for you

first lets make Ppt and Temp long formatted

city_long_df <- city_df %>%
    pivot_longer(cols = c(Ppt, Temp), names_to = "Clim", values_to = "value")
city_long_df[1:10, ]
## # A tibble: 10 × 10
##    State City       Lat  Long     Pop  Time Growth Size  Clim    value
##    <fct> <fct>    <dbl> <dbl>   <int> <int>  <dbl> <fct> <chr>   <dbl>
##  1 OR    Portland  45.5 -123. 2074775    20 11820. large Ppt   1104.  
##  2 OR    Portland  45.5 -123. 2074775    20 11820. large Temp    11.9 
##  3 OR    Eugene    44.1 -123.  273439    19 10865. small Ppt   1137.  
##  4 OR    Eugene    44.1 -123.  273439    19 10865. small Temp    11.4 
##  5 OR    Salem     44.9 -123.  266804    18  9689. small Ppt   1009.  
##  6 OR    Salem     44.9 -123.  266804    18  9689. small Temp    11.7 
##  7 OR    Medford   42.3 -123.  170876    17  8430. small Ppt    509.  
##  8 OR    Medford   42.3 -123.  170876    17  8430. small Temp    12.1 
##  9 OR    Bend      44.1 -121.  109802    16  7556. small Ppt    293.  
## 10 OR    Bend      44.1 -121.  109802    16  7556. small Temp     8.12

Using stat_summary(): make the exact bar and error plot

There’s many different fun or fun.data in stat_summary(). Here we are using mean and mean_cl_boot. Mean_cl_boot is part of library(Hmisc) and is a bootstrapped 95% confidence interval.

ggplot(data = city_long_df, aes(x = State, y = value, fill = Clim)) +
    facet_wrap(~Clim, nrow = 1, scales = "free") + stat_summary(fun = mean,
    geom = "bar") + stat_summary(fun.data = mean_cl_boot, geom = "errorbar",
    color = "black")

make the exact point and error plot with stat_summary()

ggplot(data = city_long_df, aes(x = State, y = value, colour = Clim)) +
    facet_wrap(~Clim, nrow = 1, scales = "free") + stat_summary(fun.data = mean_cl_boot,
    geom = "errorbar", color = "black") + stat_summary(fun = mean,
    geom = "point", size = 5)

Showing the full data

If you can, it is always best to show the full data. Bar charts are especially misleading.

Below is an example of why it could be important to show all the data. We will revisit code to make these figures later.

Let’s show the full data: make box plots and violin

Make a boxplot

ggplot(data = city_long_df, aes(x = State, y = value, fill = Clim)) +
    facet_wrap(~Clim, nrow = 1, scales = "free") + geom_boxplot()

make violin plot

ggplot(data = city_long_df, aes(x = State, y = value, fill = Clim)) +
    facet_wrap(~Clim, nrow = 1, scales = "free") + geom_violin(draw_quantiles = 0.5)

Now let’s show the raw data with points and mean and confidence interval

Just layer points then summary stats

ggplot(data = city_long_df, aes(x = State, y = value, colour = Clim)) +
    facet_wrap(~Clim, nrow = 1, scales = "free") + geom_point(size = 4) +
    stat_summary(fun.data = mean_cl_boot, geom = "errorbar",
        color = "black") + stat_summary(fun = mean, geom = "point",
    size = 5, colour = "black")

same figure but little bit nicer

Use geom_sina() in library(ggforce) for a violin plot style shape for raw ponts. This looks better with the more data you have.

ggplot(data = city_long_df, aes(x = State, y = value, colour = Clim)) +
    facet_wrap(~Clim, nrow = 1, scales = "free") + geom_sina(size = 3) +
    stat_summary(fun.data = mean_cl_boot, geom = "errorbar",
        color = "black") + stat_summary(fun = mean, geom = "point",
    size = 5, colour = "black")

Customizing your figure to look nicer (aka tell a better story)

Honestly, base ggplot is pretty hidieous but it is fast and has a bunch of customizable features. We already learned about about size, shape, colour, and fill. Let’s get more in depth with that.

Things covered:
- change factor label and order
- add x, y axis labels and title
- colour vs fill, different kinds of points
- limits
- scale
- theme

Changing the factors to have different name

Start with a scatter plot of Temp vs Ppt and change State names and order

Components of factor:
- levels: order but must use exact character as listed
- labels: new names in order of levels

levels(city_df$State)
## [1] "AZ" "CA" "NV" "OR" "WA"

If you don’t want to change the variable in the data frame, you can create a new one or just change the factor in the ggplot code

city_df$State_name <- factor(city_df$State, levels = c("WA",
    "OR", "CA", "NV", "AZ"), labels = c("Washington", "Oregon",
    "California", "Nevada", "Arizona"))
levels(city_df$State_name)
## [1] "Washington" "Oregon"     "California" "Nevada"     "Arizona"
ggplot(data = city_df, aes(x = Temp, y = Ppt)) + geom_point(aes(colour = State_name),
    size = 4) + stat_smooth(method = "lm", linetype = "dashed",
    colour = "black", size = 2)

use xlab(), ylab(), and ggtitle() to change labels and title

ggplot(data = city_df, aes(x = Temp, y = Ppt)) + geom_point(aes(colour = State_name),
    size = 4) + stat_smooth(method = "lm", linetype = "dashed",
    colour = "black", size = 2) + xlab("Temperature") + ylab("Precipitation") +
    ggtitle("Temp vs. Precip. by State")

colour vs. fill

Colour or color (ggplot recognizes both): points, lines, text, borders Fill: Anything with area

Points can by filled if you set the point to be empty using pch values 21-25
Point styles are changed using pch, see shapes here (http://www.sthda.com/english/wiki/ggplot2-point-shapes)

Use fill to fill empty cirlce and colour for the outline

ggplot(data = city_df, aes(x = Temp, y = Ppt)) + geom_point(aes(fill = State_name),
    size = 4, pch = 21, colour = "black") + stat_smooth(method = "lm",
    linetype = "dashed", colour = "black", size = 2) + xlab("Temperature") +
    ylab("Precipitation") + ggtitle("Temp vs. Precip. by State")

Scale: can be used to set colours, fill, shape, legends, axis limits, etc.

There are hundreds of options but they are pretty intuitive and if you have an issue, someone else has as well. Google is your best friend.

We are just going go through a couple scales.

colour vs fill

color selection is really important in figures, Jahner will cover this next week - scale_fill_manual()
- scale_colour_manual()
- scale_fill_npg() (I personally like this color palette for up to 10 discrete variables)

commands include:
- name: Legend title
- values: color codes (accepts hexcodes or colorbrewer label color)

shape
- scale_shape_manual()

commands include:
- name: Legend title
- values: pch codes

axis breaks and limits, discrete/continuous variables
- scale_x_continuous()
- scale_y_continuous()

commands include:
- name: axis title
- breaks: axis tick breaks
- label: axis break labels
- limits: axis limits
- positon: axis position
- expand: axis adjustment of margins

All in one figure, change the following in the Temp vs. Precip, coloured by State

  • change fill colours manually
  • change shape of each state
  • change axes labels,limits,breaks. Make x axis on the top

This isn’t a great looking figure but just showing what you can do with scale

ggplot(data = city_df, aes(x = Temp, y = Ppt)) + geom_point(aes(fill = State_name,
    shape = State_name), size = 4, colour = "black") + stat_smooth(method = "lm",
    linetype = "dashed", colour = "black", size = 2) + ggtitle("Temp vs. Precip. by State") +
    scale_fill_manual(name = "State:", values = c("red", "blue",
        "yellow", "green", "grey70")) + scale_shape_manual(name = "State:",
    values = c(21, 22, 23, 24, 25)) + scale_x_continuous(name = "Temperature",
    limits = c(5, 25), breaks = c(5, 10, 15, 20, 25), position = "top") +
    scale_y_continuous(name = "Precipitation", limits = c(0,
        1500), breaks = c(0, 250, 500, 1000, 1250, 1500))

Theme: changing figure layout

This is how you get even more customizable with your figure layout. theme() has many many options and even some basic premade layouts. Most of the options can change the position, size, font, face, color, etc. of about anything to do with the basic layout of the figure

Standard themes
https://ggplot2.tidyverse.org/reference/ggtheme.html

just two are:
- theme_bw()
- theme_classic()

theme_bw()

ggplot(data = city_df, aes(x = Temp, y = Ppt)) + geom_point(aes(fill = State_name,
    shape = State_name), size = 4, colour = "black") + stat_smooth(method = "lm",
    linetype = "dashed", colour = "black", size = 2) + ggtitle("Temp vs. Precip. by State") +
    scale_fill_manual(name = "State:", values = c("red", "blue",
        "yellow", "green", "grey70")) + scale_shape_manual(name = "State:",
    values = c(21, 22, 23, 24, 25)) + scale_x_continuous(name = "Temperature",
    limits = c(5, 25), breaks = c(5, 10, 15, 20, 25)) + scale_y_continuous(name = "Precipitation",
    limits = c(0, 1500), breaks = c(0, 250, 500, 1000, 1250,
        1500)) + theme_bw()

theme_classic()

ggplot(data = city_df, aes(x = Temp, y = Ppt)) + geom_point(aes(fill = State_name,
    shape = State_name), size = 4, colour = "black") + stat_smooth(method = "lm",
    linetype = "dashed", colour = "black", size = 2) + ggtitle("Temp vs. Precip. by State") +
    scale_fill_manual(name = "State:", values = c("red", "blue",
        "yellow", "green", "grey70")) + scale_shape_manual(name = "State:",
    values = c(21, 22, 23, 24, 25)) + scale_x_continuous(name = "Temperature",
    limits = c(5, 25), breaks = c(5, 10, 15, 20, 25)) + scale_y_continuous(name = "Precipitation",
    limits = c(0, 1500), breaks = c(0, 250, 500, 1000, 1250,
        1500)) + theme_classic()

More theme settings

There are two basic need-to-know commands in theme:
- element_blank(): removes that element
- element_text(): edits text

Within element_text(), there are many things that can be adjusted that are the same throughout theme and other elements of ggplot:

**Elements within theme that are commonly adjusted with *element_text():**
- axis.text
- axis.title
- axis.title.x
- axis.title.y
- axis.ticks
- plot.title
- legend.title
- legend.text

Other common adjustments:
- panel.grid
- panel.grid.major
- panel.grid.minor
- panel.border
- panel.spacing
- legend.position

Again, anything you want to know someone else has asked online. Google is your best friend

here is a theme I commonly use and just adjust as need be
I suggest playing with the theme and just seeing what happens

ggplot(data = city_df, aes(x = Temp, y = Ppt)) + geom_point(aes(fill = State_name,
    shape = State_name), size = 4, colour = "black") + stat_smooth(method = "lm",
    linetype = "dashed", colour = "black", size = 2) + scale_fill_npg(name = "State:") +
    scale_shape_manual(name = "State:", values = c(21, 22, 23,
        24, 25)) + scale_x_continuous(name = "Temperature") +
    scale_y_continuous(name = "Precipitation", limits = c(0,
        1500), breaks = c(0, 250, 500, 750, 1000, 1250, 1500)) +
    theme_bw() + theme(legend.position = "bottom", plot.title = element_text(size = 20,
    colour = "black", face = "bold"), axis.text = element_text(size = 13),
    axis.title = element_text(size = 16, colour = "black", face = "bold"),
    panel.border = element_rect(size = 1.5, colour = "black"),
    legend.title = element_text(size = 16, colour = "black",
        face = "bold", vjust = 1), legend.text = element_text(size = 13),
    panel.grid.major = element_blank(), panel.grid.minor = element_blank())

Combining multiple figure patterns: Patchwork

(patchwork website)

the easiest way to combine figures is with library(patchwork)

Another great function is ggarrange() in the package ggpubr, more customizable = harder to use.

first we need to make and assign figures to patch together

worst_plot <- ggplot(data = city_sum_df, aes(x = State, y = mean,
    fill = Clim)) + geom_bar(stat = "identity", position = "dodge") +
    ggtitle("Worst") + theme(title = element_text(size = 20,
    colour = "black", face = "bold"))
worst_plot

very_bad_plot <- ggplot(data = city_long_df, aes(x = State, y = value,
    fill = State)) + facet_wrap(~Clim, nrow = 1, scales = "free") +
    stat_summary(fun = mean, geom = "bar") + stat_summary(fun.data = mean_cl_boot,
    geom = "errorbar", color = "black") + scale_fill_npg() +
    ggtitle("Very bad") + theme(legend.position = "none", title = element_text(size = 20,
    colour = "black", face = "bold"))
very_bad_plot

bad_plot <- ggplot(data = city_long_df, aes(x = State, y = value,
    fill = State)) + facet_wrap(~Clim, nrow = 1, scales = "free") +
    geom_boxplot() + scale_fill_npg() + ggtitle("eh, bad") +
    theme(legend.position = "none", title = element_text(size = 20,
        colour = "black", face = "bold"))
bad_plot

okay_plot <- ggplot(data = city_long_df, aes(x = State, y = value,
    fill = State)) + facet_wrap(~Clim, nrow = 1, scales = "free") +
    geom_sina(size = 4, pch = 21) + stat_summary(fun.data = mean_cl_boot,
    geom = "errorbar", color = "black") + stat_summary(fun = mean,
    geom = "point", size = 5, colour = "black") + ggtitle("Okay") +
    theme(legend.position = "none", title = element_text(size = 20,
        colour = "black", face = "bold"))
okay_plot

Patchwork: 2 rows and 2 colums

worst_plot + very_bad_plot + bad_plot + okay_plot + plot_layout(ncol = 2,
    nrow = 2)

patchwork: 2 col, 1 row

better_plot <- ggplot(data = city_long_df, aes(x = State, y = value,
    fill = State)) + facet_wrap(~factor(Clim, labels = c("Precipitation",
    "Temperature")), nrow = 1, scales = "free") + geom_sina(size = 4,
    pch = 21) + stat_summary(fun.data = mean_cl_boot, geom = "errorbar",
    color = "black", width = 0.3, size = 1.4) + stat_summary(fun = mean,
    geom = "point", size = 7, colour = "black", pch = 22, fill = "white") +
    scale_fill_npg() + ggtitle("Better") + theme_bw() + theme(legend.position = "None",
    plot.title = element_text(size = 26, colour = "black", face = "bold"),
    axis.text = element_text(size = 18), axis.title = element_text(size = 22,
        colour = "black", face = "bold"), panel.border = element_rect(size = 1.5,
        colour = "black"), legend.title = element_text(size = 22,
        colour = "black", face = "bold", vjust = 1), legend.text = element_text(size = 18),
    panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
    strip.text.x = element_text(size = 22, face = "bold"), strip.background = element_rect(size = 1.5,
        colour = "#333333", fill = "#CCCCCC"))
better_plot

okay_plot + better_plot

sometimes it takes two figures to make one good plot

#### TEMP ####
temp_df <- city_long_df[which(city_long_df$Clim == "Temp"), ]
temp_plot <- ggplot(data = temp_df, aes(x = State, y = value,
    fill = State)) + geom_sina(size = 6, pch = 21, colour = "black") +
    stat_summary(fun.data = mean_cl_boot, geom = "errorbar",
        color = "black", width = 0.3, size = 1.4) + stat_summary(fun = mean,
    geom = "point", size = 9, colour = "black", pch = 22, fill = "white") +
    scale_fill_npg() + ylab("Temperature") + theme_bw() + theme(legend.position = "None",
    plot.title = element_text(size = 26, colour = "black", face = "bold"),
    axis.text = element_text(size = 18), axis.title = element_text(size = 22,
        colour = "black", face = "bold"), axis.title.x = element_blank(),
    panel.border = element_rect(size = 1.5, colour = "black"),
    legend.title = element_text(size = 22, colour = "black",
        face = "bold", vjust = 1), legend.text = element_text(size = 18),
    panel.grid.major = element_blank(), panel.grid.minor = element_blank())

#### PPT ####
ppt_df <- city_long_df[which(city_long_df$Clim == "Ppt"), ]
ppt_plot <- ggplot(data = ppt_df, aes(x = State, y = value, fill = State)) +
    geom_sina(size = 6, pch = 21, colour = "black") + stat_summary(fun.data = mean_cl_boot,
    geom = "errorbar", color = "black", width = 0.3, size = 1.4) +
    stat_summary(fun = mean, geom = "point", size = 9, colour = "black",
        pch = 22, fill = "white") + scale_fill_npg() + scale_y_continuous(name = "Precipitation",
    position = "right", breaks = c(250, 500, 750, 1000, 12)) +
    theme_bw() + theme(legend.position = "None", plot.title = element_text(size = 26,
    colour = "black", face = "bold"), axis.text = element_text(size = 18),
    axis.title = element_text(size = 22, colour = "black", face = "bold"),
    axis.title.x = element_blank(), panel.border = element_rect(size = 1.5,
        colour = "black"), legend.title = element_text(size = 22,
        colour = "black", face = "bold", vjust = 1), legend.text = element_text(size = 18),
    panel.grid.major = element_blank(), panel.grid.minor = element_blank())

# combine
bestest_plot <- temp_plot + ppt_plot
bestest_plot

Look at worst vs bestest

make good choices!

worst_plot + bestest_plot